The invention relates to data processing systems and, more specifically, to a computerized traffic management system for optimizing vehicular flow in complex road systems.
A long-standing problem in traffic engineering is to optimize the flow of vehicles through a given road network. A major component of advanced traffic management for complex road systems is the timing strategy for the signalized intersections. Improving the timing of the traffic signals in the network is generally the most powerful and cost-effective means of achieving this goal.
Through use of an advanced transportation management system, that includes sensors and computer-based control of traffic lights, a municipality seeks to more effectively use the infrastructure of the existing transportation network, thereby avoiding the need to expand infrastructure to accommodate growth in traffic. It appears that much of the focus to date has been on the hardware (sensors, detectors, and other surveillance devices) and data processing aspects. In fact, however, the advances in these areas will be largely wasted unless they are coupled with appropriate analytical techniques for adaptive control.
Because of the many complex aspects of a traffic system, e.g., human behavioral considerations, vehicle flow interactions within the network, weather effects, traffic accidents, long-term (e.g., seasonal) variation, etc., it has been notoriously difficult to determine the optimal signal timing. This is an extremely challenging control problem at a system (network)-wide (multiple intersection) level. Much of the signal timing difficulty has stemmed from the need to build extremely complex models of the traffic dynamics as a component of the control strategy.
System-wide control is the means for real-time (demand-responsive) adjustment of the timings of all signals in a traffic network to achieve a reduction in overall congestion consistent with the chosen system-wide measure of effectiveness (MOE). This real-time control is responsive to instantaneous changes in traffic conditions, including changes due to accidents or other traffic incidents. Further, the timings should change automatically to adapt to long-term changes in the system (e.g. street reconfiguration or seasonal variations). To achieve true system-wide optimality, the timings at different signals will not generally have a predetermined relationship to one another (except notably for those signals along one or more arteries within the system where it is desirable to synchronize the timings).
All known attempts for real-time demand responsive control either are optimized only on a per-intersection basis or make simplifying assumptions to treat the multiple-intersection problem. An example of the former is OPAC described in Gartner, N. H., Tarnoff, P. J., and Andrews, C. M. (1991), "Evaluation of Optimized Policies for Adaptive Control Strategy," Transportation Research Record 1324, pp. 105-114, while examples of the latter include SCOOT described in Hunt, P. B., Robertson, D. I., Bretherton, R. D. and Winton, R. I. (1981), "SCOOT--A Traffic Responsive Method of Coordinating Signals," Transport and Road Research Lab., Crowthorne, U. K., Rep. LR 1014 and Martin, P. J. and Hockaday, S. L. M. (1995), "SCOOT--An Update," ITE Journal, January 1995, pp. 44-48, and REALBAND described in Dell'Olmo, P. and Mirchandani, P. (1995), "An Approach for Real-Time Coordination of Traffic Flows on Networks," Transportation Research Board Annual Meeting, Jan. 22-28, 1993, Washington, D.C., Paper no. 950837.
The SCOOT method's version of system-wide control differs from the above definition of system-wide control in that it tends to lump cycle length adjustment for groups of intersections into single parameters, and thus the option of full independent signal adjustments is not completely available. SCOOT's system-wide (i.e. multiple, interconnecting artery) approach is limited to broad strategy choices from one traffic corridor to another rather than a coordinated set of signal parameter selections for the entire network. Hence, although SCOOT may be implemented on a full traffic system, it is not a true system-wide controller in the sense considered here.
The other multiple intersection technique mentioned above, REALBAND, provides a way to improve platoon progression, which the other techniques apparently lack. However, REALBAND is limited in its application to types of traffic patterns for which vehicle platoons are easily identifiable and, thus, may not perform well in heavily congested conditions with no readily identifiable platoons. Finally, neither of these techniques incorporates a method to automatically self-tune over a period of weeks or months.
The essential ingredient in all previous attempts to provide signal timings for single or multiple intersections is a model for the traffic behavior. However, the problem of fully modeling traffic at a system-wide level is daunting.
In the OPAC, SCOOT, and REALBAND approaches discussed above, the models used are in the form of traditional equation-based relationships, but it is also possible to use other model representations such as a neural network, fuzzy associative memory matrix or rules base for an expert system. The signal timings are then based on relationships (algebraic or otherwise) derived from the assumed model of the traffic dynamics. For real-time (demand-responsive) approaches, this relationship (or "control function") takes information about current traffic conditions as input and produces as output the timings for the signals. However, to the extent that the traffic dynamics model is flawed or oversimplified, the signal timings will be suboptimal.
The application of neural networks (NNs) to traffic control has been proposed and examined by, e.g., Dougherty, M., Kirby, H., and Boyle, R. (1993), "The Use of Neural Networks to Recognize and Predict Traffic Congestion," Traffic Engineering and Control, pp. 311-314 and in Nataksuji, T. and Kaku, T. (1991), "Development of a Self-Organizing Traffic Control System Using Neural Network Models," Transportation Research Record, 1324, TRB, National Research Council, Washington, D.C., pp. 137-145. These NN-based control strategies still require a model (perhaps a second NN) for the traffic dynamics, which is usually constructed off-line using system historical data.
This would also apply to controllers based on principles of fuzzy logic or expert systems (e.g., Kelsey, R. L. and Bisset, K. R. (1993), "Simulation of Traffic Flow and Control Using Fuzzy and Conventional Methods," Fuzzy Logic and Control (Jamshidi, M., et al., eds.), Prentice Hall, Englewood Cliffs, N.J., Chapter 12, and Ritchie, S. G. (1990), "A Knowledge-Based Decision Support Architecture for Advanced Traffic Management," Transportation Research-A, vol. 24A, pp. 27-37). For both of these approaches, there is still a need for a system model (aside from a control model). In these approaches, the system model is not a set of equations, but instead is a detailed list of rules that express "if-then" relationships (either directly or through a so-call fuzzy associative memory matrix). Similar to other model-based controllers, these "if-then" relationships must be determined initially and periodically recalibrated.
The extreme difficulty in mathematically describing such critical elements of the traffic system as complex flow interactions among the arteries in the presence of traffic congestion, weather-related changes in driving patterns, flow changes as a result of variable message signs or radio announcements, etc., will inherently limit any control strategy that requires a model of the traffic dynamics. Related to this is the non-robustness of system model-based controls to operational traffic situations that differ significantly from situations represented in the data used to build the system model (this non-robustness can sometimes lead to unstable system behavior). Further, even if a reliable system model could be built, a change to the scenario or measure-of-effectiveness (MOE) would typically entail many complex calculations to modify the model and requisite optimization process.
In addition to the above considerations, system-wide control (as defined above) requires that the controller automatically adapt to the inevitable long-term (say, month-to-month) changes in the system. This is a formidable requirement for the current model-based controllers as these long-term changes encompass difficult-to-model aspects such as seasonal variations in flow patterns on all links in the system, long-term construction blockages or lane reconfiguration, changes in the number of residences and/or businesses in the system, etc. In fact, in the context of the Los Angeles traffic system, the difficulty and high costs involved in adapting to long-term system changes is a major limitation of current traffic control strategies.
In sum, there exists a need for a traffic control approach that can achieve optimal system-wide control in a complex road system by automatically adapting to both daily non-recurring events (accidents, temporary lane closures, etc.) and to long-term evolution in the transportation system (seasonal effects, new infrastructure, etc.).